Basic Memory alternatives: which record do you need?
Considering a Basic Memory alternative? Choose local Markdown and a graph, browser-first notes, hosted team knowledge, or durable task state for AI work.
Basic Memory is a strong choice if you want a Markdown knowledge base that can stay on your machine, work offline, and grow into a searchable graph. An alternative makes sense when the record you need is different: browser-first personal notes, hosted shared knowledge, or project and task state that an AI client can read beside the context.
That is the decision. The products overlap because they all connect AI clients to information you can inspect. They differ in what they make central. A note is one kind of record. A project handoff is another.
| If you need | Start with | Why |
|---|---|---|
| Local Markdown, offline work, and a graph over your notes | Basic Memory | Its open-source local edition keeps the knowledge base on your machine. |
| A personal Markdown library you edit in a browser and review after AI changes | OneKB | It pairs a web editor with versioned AI writes and an audit ledger. |
| Hosted Markdown notes for people and AI clients across a team | Hjarni | Its first-party remote MCP server is built around shared knowledge, folders, and permissions. |
| Project context and current work records for AI clients | vtriv | Its projects keep Markdown, tasks, assignments, status, and discussion together. |
The broader field is in our MCP memory server comparison. This page answers the narrower question that comes up after Basic Memory is already on the shortlist.
Basic Memory is the local-first choice
Basic Memory stores notes as Markdown, indexes them for search, and connects observations and relations into a knowledge graph. Its local edition is open source and runs on your machine. That matters when offline access, a folder you can put under version control, or control over where the files live comes before browser access.
Basic Memory supports frontmatter-based search, schemas, relations, and tools for an AI client to read, write, search, and build context from your notes. If you want an agent to recover a past design decision and follow its links to the supporting material, that graph-shaped model is useful.
Basic Memory Cloud changes the tradeoff without discarding the local path. It offers a hosted MCP endpoint, a web app, project ZIP import and export, optional local sync, and snapshots. The same Markdown format can move between local and cloud. Its cloud guide describes that split in detail.
Choose Basic Memory when knowledge itself is the center of the work. You want a durable note base that is local by default, structured enough to query, and available to whichever MCP client you connect.
The question to ask before leaving is simple: does a note schema carry the work you need to hand off? For personal tasks, perhaps. For a team that needs explicit ownership, current status, dependencies, and a discussion tied to a project, a note can become a poor substitute for a work record.
OneKB is for browser-first personal notes
OneKB keeps its notes as plain Markdown and gives them a browser home. You can write and organize the library from a phone or desktop browser, export it as a ZIP, and let a connected AI client create, edit, rename, move, and search notes.
Its sharpest distinction is the trail around AI changes. OneKB versions each AI write, records tool calls in an audit ledger, sends deletions to trash, and supports rollback. That is useful when you want to inspect what an assistant changed from the web interface instead of checking a local file history.
The MCP connection is a local npx server configured with a scoped API key, according to OneKB’s MCP guide. That fits a person who works in Claude Code, Cursor, or another terminal-based MCP client and wants the human side of the library available in a lightweight web app.
Choose OneKB when the record is a personal note library and browser editing or AI-change review matters more than an offline local graph. It is deliberately narrow. You will want another system if the core problem is team work tracking or a shared project handoff.
Hjarni is for hosted shared knowledge
Hjarni is a hosted Markdown knowledge base with a first-party remote MCP server. Claude and ChatGPT can connect to the same notes, and teams can share folders with viewer, editor, and admin roles. Its MCP surface reads, writes, searches, organizes notes, and manages tags, containers, attachments, and wiki-links.
This is a clean choice when the knowledge needs to be available from any device without running a local service or synchronizing a folder. A team can keep its runbooks, decisions, and customer context in one hosted place while each person uses the AI client they prefer.
Hjarni says plainly that it is knowledge management rather than project management. That clarity helps. Choose it for a hosted note base with remote MCP access. Move to a task-centered workspace only when the next AI session needs to see the current work as more than a collection of notes.
vtriv is for context with a live work record
vtriv is useful when an AI client needs more than retained notes. A project can keep its brief and reference material in Markdown, then carry task records with a description, assignee, status, dependencies, checklist, attachments, comments, and custom fields. The context explains the work; the task says where the work stands.
Imagine a team moving an integration. The project record has the vendor constraint and the decision behind the migration. A task named “Confirm the empty export message” is assigned to an agent, marked blocked by the endpoint work, and carries a comment from the last test. A connected client can read that state through vtriv’s hosted MCP connection. An external runner may query the assigned, unblocked tasks and decide what to work on next.
vtriv stores that shared state. The runner decides when to act, which model and tools to use, and how to perform the work. Assignment records responsibility; it does not start a job. Assigned tasks for AI agents covers why that boundary keeps a handoff readable.
Choose vtriv when the work needs a durable record beside the knowledge: a current project, named task ownership, status, dependencies, and a discussion the next client can inspect. If you only need a personal Markdown graph, Basic Memory is a better fit. If you only need hosted team notes, Hjarni is a better fit. Those are good outcomes. The record should match the work.
Start small in vtriv: create one project, add the context a future session would otherwise need repeated, and make one task that names a real outcome. A connected client will have a useful place to begin.